PRESENTERS:Jason Chu is
the Senior Education Manager at Turnitin. His focus is on working to
build resources for educators, and his personal passion is to find better
ways to enhance student achievement. He analyzed the statistics from the
Turnitin database that is the basis for this webcast.

Renee Bangerter is a Professor of English at
Saddleback College and has been teaching writing for 15 years. She
collaborates with K-12 teachers developing curriculum to bridge the gap
between high school and college in reading, writing and grammar. Renee
creates the professional development webinars for Turnitin, emphasizing
how instructors can innovatively teach students about plagiarism.

Summer Dittmer has
taught high school Drama, Online Expository Writing, English and Honors
English for 11 years. Aside from teaching 12th grade Literature, she is
the Student Activities Coordinator at Mercy High School in Burlingame,
CA. She has trained teachers in developing online curriculum, along with
assisting in implementing effective one-to-one iPad programs.

Webcast
Overview

Join
us as we share updated findings on the types of internet sources
that students use in their written work, discuss the implications of
the findings and share best practices to help prevent plagiarism in the
classroom.

The 30-Minute Webcast Series from Turnitin is for busy
educators who want to stay current with the latest trends and
technologies on preserving academic integrity, improving student
writing across the curriculum, and promoting student success.

Is Intellectual Curiosity a Strong Predictor for Academic
Performance?

What Does Research Tell us about Academic Performance and
Curiosity?

For better or worse, academic performance has long stood
as a proxy for general aptitude. To understand what factors affect academic
performance gives us a better understanding of how instructors can help
students achieve their greatest potential in college. Empirical evidence
strongly suggests that academic performance can be predicted by a combination
of cognitive ability (or intelligence) and effort. Non-ability personality
traits, such as effort, can be potentially more meaningful than intelligence
because less able students can compensate for lower levels of cognitive ability
by becoming more conscientious, studying harder, and paying greater attention
to details and rules. Beyond cognitive ability and effort, researchers look to
so-called investment traits to explain inter-individual differences among
people?s drive to pursue, enjoy, and engage in learning opportunities.
Intellectual curiosity conveniently describes this impulse, as does the
researcher?s titular phrase,the hungry
mind.? Like cognitive ability and effort, intellectual curiosity positively
associates (at medium effect sizes) with academic performance.

Several instruments have been developed to measure
something like curiosity. The ?Need for Cognition? scale measures the ?tendency
for an individual to engage in and enjoy thinking? (Cacioppo & Petty, 1982,
p. 116). The ?Typical Intellectual Engagement? (TIE) scale was designed to
?differentiate among individuals in their typical expression of a desire to
engage and understand their world, their interest in a wide variety of things,
and their preference for a complete understanding of a complex topic or
problem, a need to know?.? (Goff and Ackerman, 1992, p. 539). Because these
measures have similar conceptual underpinnings and share criteria validity for
academic performance and intelligence, they appear to measure the same trait
dimension, and studies that use these scales can be rolled into a meta-analysis.

Study & Methods

To investigate whether curiosity is a strong determinant
for academic performance, Von Stumm, Hell, & Chamorro-Premuzic extracted
correlation coefficients from three previous studies and performed four
meta-analyses that focused on TIE to stand in for curiosity. For the new TIE
meta-analyses, the researchers selected 11 studies (including several in which
one of the authors had participated). They excluded studies that did not
include empirical data, did not include zero-order correlations, or included
previously reported data. In these studies, academic performance was expressed
as either grade point average or an academic performance composite. From the
extracted correlation coefficients and the new meta-analyses, the authors
created five path models using a stepwise process, settling on a single,
best-fit model.

Findings

The best fitting model indicated that intelligence, TIE,
and conscientiousness were direct and intercorrelated predictors of academic
performance. Within this model, intelligence (.35) accounted for the greatest
amount of variance while curiosity (.20) and effort (.20) had slightly smaller
and equal and independent impacts on academic performance. (The preceding
measurements are standardized beta weights.) The authors thus confirm their
hypothesis that intellectual investment, including curiosity, is a key
determinant of academic performance.

Discussion and Implications

The authors suggest several important ramifications of
this finding:

? Academic performance can be increased if students?
intellectual curiosity is regularly renewed and stimulated. Thus, students
should be

encouraged to follow challenging paths and not be
exclusively rewarded for their ?acquiescent application of intelligence and
effort.? Universities

and colleges should seek to exploit opportunities to
inspire curiosity and reward productive novelty.

? Admissions officers should pay attention to
intellectual curiosity as a strong predictor of potential.

? Future studies to examine predictors of academic
success should seek to expand their range beyond intelligence and effort.

Implications

Technology may have a role in cultivating curiosity by
providing greater access to new information, new ways to participate in culture
through new media (Jensen et al, 2006), and novel methods of visualizing data.
Curiosity might also have a role to play in orienting students toward life-long
learning, which has already been shown to be influenced by such pedagogical
practices as active learning, reflection, and tasks that encourage
perspective-taking (Mayhew, M.J., Wolniak, G.C., & Pascarella, 2008).

Study Limitations

As the authors note, the study is constrained by several
factors, including the quality of the original studies in the meta-analyses.
Further, only conscientiousness was used as a proxy for effort, ignoring
academic motivation, self-efficacy, and ambition. The study also did not
consider the cumulative effect of success as an ongoing magnifier for
conscientiousness and curiosity. To correct for this, another study would have
to consider the longitudinal effects of an academic course of study and not a
single moment. Finally, the authors concede that despite the encouraging
results that showed that conscientiousness and intellectual curiosity combined
influenced academic performance to the same degree as intelligence, other
variables likely to have an effect, such as choice of subject, socio-economic
status, self-confidence, etc., were not factored into a final model. Seen in
the context of these limitations, the study directs researchers to continue to
explore the nexus of no

A whole new class of specialized learning
tools and technologies are appearing in classrooms today and are expected
to increase in the near future. This influx of new classroom technology
is driven by Common Core and other curriculum standards, student demand
for cutting-edge technology and the need to produce a more globally
competitive workforce.

Specialty classroom technologies are also
being used in the area of workforce readiness. Campuses face challenges
both in trying to interest students in certain fields that are hiring as
well as to retain them through graduation. However, a whole new class of
specialized technology is emerging that not only can make up for campus’
limited resources, but can spark student engagement.

To equip students with the skills they need,
K-12 and Higher Education institutions are now employing intensive and
often specialized technologies such as:

Math and science labs

Gaming, animation and
media programming labs

Art labs

GIS/CAD labs

Project-based learning
environments

Virtual models and
simulation games

Special needs programs

This webinar will discuss how these technologies and others
are being implemented to deliver near real-world experiences to students
in schools around the country. Please join us!

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Welcome to the Dillard University Center for Teaching, Learning and Academic Technology Blog!

The Dillard University Center for Teaching, Learning and Academic Technology (DUCTLAT) http://www.ductlat.blogspot.com/ is essential in helping faculty effectively manage technology on and off campus. An important aspect of this initiative is achieved by bringing groups of faculty members together to learn the technology necessary to develop electronic resources for their courses and themselves, while planning and beginning the development of shared curricular materials. Cultivating faculty success at all levels reinforces the University's commitment to build premier faculty learning communities. All faculty, deans and academic administrators play a critical role in creating competitive and productive academic units in which expectations for faculty performance are clearly articulated and professional achievements are recognized and rewarded.